TY - GEN
T1 - Sample-Constrained Black Box Optimization for Audio Personalization
AU - Rajagopalan, Rajalaxmi
AU - Wei, Yu Lin
AU - Choudhury, Romit Roy
N1 - Publisher Copyright:
Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2024/3/25
Y1 - 2024/3/25
N2 - We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter h∗, which applied to any music or speech, will maximize the user's satisfaction. This is a black-box optimization problem since the user's satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter hi, and query the user for their satisfaction scores f(hi). A family of “surrogate” functions is then designed to fit these scores and the optimization method gradually refines these functions to arrive at the filter ĥ∗ that maximizes satisfaction. In certain applications, we observe that a second type of querying is possible where users can tell us the individual elements h∗[j] of the optimal filter h∗. Consider an analogy from cooking where the goal is to cook a recipe that maximizes user satisfaction. A user can be asked to score various cooked recipes (e.g., tofu fried rice) or to score individual ingredients (say, salt, sugar, rice, chicken, etc.). Given a budget of B queries, where a query can be of either type, our goal is to find the recipe that will maximize this user's satisfaction. Our proposal builds on Sparse Gaussian Process Regression (GPR) and shows how a hybrid approach can outperform any one type of querying. Our results are validated through simulations and real world experiments, where volunteers gave feedback on music/speech audio and were able to achieve high satisfaction levels. We believe this idea of hybrid querying opens new problems in black-box optimization and solutions can benefit other applications beyond audio personalization.
AB - We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter h∗, which applied to any music or speech, will maximize the user's satisfaction. This is a black-box optimization problem since the user's satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter hi, and query the user for their satisfaction scores f(hi). A family of “surrogate” functions is then designed to fit these scores and the optimization method gradually refines these functions to arrive at the filter ĥ∗ that maximizes satisfaction. In certain applications, we observe that a second type of querying is possible where users can tell us the individual elements h∗[j] of the optimal filter h∗. Consider an analogy from cooking where the goal is to cook a recipe that maximizes user satisfaction. A user can be asked to score various cooked recipes (e.g., tofu fried rice) or to score individual ingredients (say, salt, sugar, rice, chicken, etc.). Given a budget of B queries, where a query can be of either type, our goal is to find the recipe that will maximize this user's satisfaction. Our proposal builds on Sparse Gaussian Process Regression (GPR) and shows how a hybrid approach can outperform any one type of querying. Our results are validated through simulations and real world experiments, where volunteers gave feedback on music/speech audio and were able to achieve high satisfaction levels. We believe this idea of hybrid querying opens new problems in black-box optimization and solutions can benefit other applications beyond audio personalization.
UR - http://www.scopus.com/inward/record.url?scp=85189303273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85189303273&partnerID=8YFLogxK
U2 - 10.1609/aaai.v38i9.28881
DO - 10.1609/aaai.v38i9.28881
M3 - Conference contribution
AN - SCOPUS:85189303273
T3 - Proceedings of the AAAI Conference on Artificial Intelligence
SP - 10164
EP - 10171
BT - Technical Tracks 14
A2 - Wooldridge, Michael
A2 - Dy, Jennifer
A2 - Natarajan, Sriraam
PB - Association for the Advancement of Artificial Intelligence
T2 - 38th AAAI Conference on Artificial Intelligence, AAAI 2024
Y2 - 20 February 2024 through 27 February 2024
ER -